Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

Abstract

The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.

Fast and Accurate Short-Term Load Forecasting with a Hybrid Model

“…Here, H represents the concatenation of the hidden layer output features and input nodes via direct links. As earlier explained, the input node directly connected to the output node via direct link does not require assigning any weights as depicted in Figure 1 [30,33,42]. The addition of these direct links in RVFL serves to regularize the model, resulting in improved generalization performance and lower model complexity.…”

“…An independently developed method, the single hidden layer neural network with random weights (RWSLFN), was reported in [29], differing from RVFL by excluding the direct links. Research has shown that the direct links significantly enhances RVFL's performance, especially in time series forecasting [27,[30][31][32][33]. In [16,31,33], the authors clearly demonstrated that the presence of the direct links in RVFL help to regularize the randomization and reduce the model complexity.…”

“…Research has shown that the direct links significantly enhances RVFL's performance, especially in time series forecasting [27,[30][31][32][33]. In [16,31,33], the authors clearly demonstrated that the presence of the direct links in RVFL help to regularize the randomization and reduce the model complexity.…”